Deep Learning Analysis of Viral Genome Evolution in SARS Family and Ebola

Abstract

Infectious diseases remain a hindrance to global health with the pace of anthropogenic, behavioral, and social change fostering emergence and expansion of new pathogens. In this regard, insights into high-impact virus genomic structure and evolutionary processes are important for informed public health interventions. The current study offers an exhaustive comparative examination of the four significant viruses' genomic sequences: COVID-19, SARS, MERS, and Ebola. High-throughput genomic sequencing methods were used in combination with a Gated Recurrent Unit (GRU)-solved deep learning algorithm to decipher the intricate genetic characteristics of the pathogens. The comparative genomics strategy identifies both conserved and shared genetic features between the viruses and highlights their transmission mechanisms, host interactions, and pathogenicity. As SARS-CoV-2 has high sequence identity with SARS-CoV and MERS-CoV, unique spike protein structural differences and receptor-binding domains account for variability in infectivity and host range. Integration of deep learning allows for sophisticated pattern detection in large-scale genomic data, improving the accuracy of evolutionary and functional inference. The GRU model showed excellent performance, with accuracy levels of 99.01% for COVID-19, 98.91% for SARS, 98.35% for MERS, and 98.04% for Ebola, with high recall, precision, and F1-scores, highlighting the model's credibility for viral genome prediction. These findings support the model's application in differentiating between highly similar viral genomes and extracting principal genetic signatures of importance for diagnostics and therapeutics. This research not only expands our knowledge of emerging viruses' genetic associations and evolutionary pathways but also highlights the promise of machine learning-based genomic analysis as a preparedness and response strategy against future epidemics. The results provide critical information for the design of focused diagnostics, vaccines, and antiviral measures, emphasizing the role of genomic surveillance in global health security.